Although most GWAS gene set analyses are discovery projects, careful attention still needs to be paid to guard against spurious findings so that resources can be efficiently allocated to subsequent genotyping, re-sequencing and functional studies. As mentioned above, these biases may stem from gene length (the number of SNPs in a gene), gene set size (the number of genes in a gene set), overlapping genes, LD patterns, and population stratifications. In addition, any selection process during data processing (e.g., selecting the most significant SNP to represent each gene) should be accounted for in the final tests. The impact of several potential sources of bias need to be evaluated for gene set analysis methods. When two or more GWAS datasets are available for the same disease or phenotype, to minimize the bias, we suggest investigators use one dataset as the discovery dataset and the other(s) as validation dataset(s) [26].
